Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in the United States and the rest of the developed world, and has serious morbidity and mortality. Because AF has several variants, is multi- factorial, and evolves over time, it is very difficult and expensive to study comprehensively in large-animal models, in part due to the inherent technical difficulties of imaging whole-atria electrophysiology in vivo. Predictive multiscale computational modeling has the potential to fill this research void. While we and others have performed some early-stage multiscale modeling of AF, the community would benefit greatly from a systematic modeling framework with which to illuminate the many facets of this complex disorder. The overall goal of this project is to develop a multiscale modeling framework that will enable the evaluation of potential pharmacological and device-based atrial fibrillation therapies. In addition to developing such a modeling system, as a proof-of-concept of its therapy-design utility we will collect and utilize cellular electrophysiological data to predict the efficacy of pharmacological agents at controlling paroxysmal, persistent, and chronic AF in the presence of common human ion-channel polymorphisms. Specifically, we aim: 1. To develop a straightforward, extensible framework capable of modeling the human atria. 2. To implement a multiscale model of atrial fibrillation representing the multiple states of the disorder. 3. To acquire electrophysiological data of the impact of common ion channel gene polymorphisms on drug- channel interactions. 4. To predict pharmacological AF control efficacy in the realistic atrial model(s) with incorporated ion channel gene polymorphisms. This project will produce an extensible, open-source atrial fibrillation modeling framework that will be useful not only to test the specific question of pharmacological efficacy in the context of ion channel polymorphisms, but also for the modeling community at large to investigate the vast array of issues surrounding atrial fibrillation and its therapy. PUBLIC HEALTH RELEVANCE: (Using no more than two or three sentences, describe the relevance of this research to public health.) Atrial fibrillation is the most common sustained cardiac arrhythmia in the United States and the rest of the developed world, and has serious mortality and morbidity. Because atrial fibrillation is very difficult and expensive to study comprehensively in large-animal models, the research community would benefit greatly from a systematic computational modeling framework with which to illuminate the many facets of this complex disorder. The overall goal of this project is to develop such a multiscale modeling framework, which will enable the evaluation of potential pharmacological and device-based atrial fibrillation therapies.